2026-04-17T18:50:46Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/75362023-04-18T10:42:52Zcom_10259_4141com_10259_5086com_10259_2604com_10259_4219com_10259_4996com_10259_3989com_10259.4_106com_10259_5377com_10259_6688col_10259_4142col_10259_4220col_10259_6689col_10259_4999col_10259_5378
A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept
Ramírez Sanz, José Miguel
Garrido Labrador, José Luis
Olivares Gil, Alicia
García Bustillo, Álvaro
Arnaiz González, Álvar
Diez Pastor, José Francisco
Jahouh, Maha
González Santos, Josefa
González Bernal, Jerónimo
Allende-Río, Marta
Valiñas Sieiro, Florita
Trejo Gabriel y Galán, José Mª
Cubo Delgado, Esther
Parkinson’s disease
Telerehabilitation
Telemedicine
Big data
Artificial intelligence in healthcare
The consolidation of telerehabilitation for the treatment of many diseases over the last
decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in
remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to
unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises
and proper corporal movements online should also be mentioned. The focus of this paper is on
a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and
other less accessible locations. A full-stack is presented using big data frameworks that facilitate
communication between the patient and the occupational therapist, the recording of each session,
and real-time skeleton identification using artificial intelligence techniques. Big data technologies are
used to process the numerous videos that are generated during the course of treating simultaneous
patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for
automated evaluation of corporal exercises, which is of immense help to the therapists in charge of
the treatment programs.
2023-03-13
2023-03-13
2023-02
info:eu-repo/semantics/article
http://hdl.handle.net/10259/7536
10.3390/healthcare11040507
2227-9032
eng
Healthcare. 2023, V. 11, n. 4, 507
https://doi.org/10.3390/healthcare11040507
info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII)/PI19%2F00670/ES/ESTUDIO DE FACTIBILIDAD Y COSTE-EFECTIVIDAD DEL USO TELEMEDICINA CON UN EQUIPO MULTIDISCIPLINAR PARA PREVENCION DE CAIDAS EN LA ENFERMEDAD DE PARKINSON/
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Atribución 4.0 Internacional
MDPI